Breaking through the 80% glass ceiling: Raising the state of the art in word sense disambiguation by incorporating knowledge graph information
M Bevilacqua, R Navigli - Proceedings of the conference …, 2020 - iris.uniroma1.it
Neural architectures are the current state of the art in Word Sense Disambiguation (WSD).
However, they make limited use of the vast amount of relational information encoded in …
However, they make limited use of the vast amount of relational information encoded in …
Flaubert: Unsupervised language model pre-training for french
Language models have become a key step to achieve state-of-the art results in many
different Natural Language Processing (NLP) tasks. Leveraging the huge amount of …
different Natural Language Processing (NLP) tasks. Leveraging the huge amount of …
[PDF][PDF] Recent trends in word sense disambiguation: A survey
M Bevilacqua, T Pasini… - … Joint Conference on …, 2021 - researchportal.helsinki.fi
Abstract Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word
in context by identifying the most suitable meaning from a predefined sense inventory …
in context by identifying the most suitable meaning from a predefined sense inventory …
GlossBERT: BERT for word sense disambiguation with gloss knowledge
Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a
particular context. Traditional supervised methods rarely take into consideration the lexical …
particular context. Traditional supervised methods rarely take into consideration the lexical …
A survey on semantic processing techniques
Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …
era of powerful pre-trained language models and large language models, the advancement …
Does BERT make any sense? Interpretable word sense disambiguation with contextualized embeddings
Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al., 2018),
Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in …
Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in …
Moving down the long tail of word sense disambiguation with gloss-informed biencoders
T Blevins, L Zettlemoyer - arXiv preprint arXiv:2005.02590, 2020 - arxiv.org
A major obstacle in Word Sense Disambiguation (WSD) is that word senses are not
uniformly distributed, causing existing models to generally perform poorly on senses that are …
uniformly distributed, causing existing models to generally perform poorly on senses that are …
With more contexts comes better performance: Contextualized sense embeddings for all-round word sense disambiguation
Contextualized word embeddings have been employed effectively across several tasks in
Natural Language Processing, as they have proved to carry useful semantic information …
Natural Language Processing, as they have proved to carry useful semantic information …
ConSeC: Word sense disambiguation as continuous sense comprehension
Supervised systems have nowadays become the standard recipe for Word Sense
Disambiguation (WSD), with Transformer-based language models as their primary …
Disambiguation (WSD), with Transformer-based language models as their primary …
Sensembert: Context-enhanced sense embeddings for multilingual word sense disambiguation
Contextual representations of words derived by neural language models have proven to
effectively encode the subtle distinctions that might occur between different meanings of the …
effectively encode the subtle distinctions that might occur between different meanings of the …